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Original article
Athletic groin pain ( part 2): a prospective cohort
study on the biomechanical evaluation of change
of direction identifies three clusters of movement
patterns
A Franklyn-Miller,1,2 C Richter,1 E King,1,3 S Gore,1,4 K Moran,4,5 S Strike,3
E C Falvey1,6
►► Additional material is
published online only. To view
please visit the journal online
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1
Sports Medicine Research
Department, Sports Surgery
Clinic, Santry Demesne, Dublin,
Ireland
2
Centre for Health, Exercise
and Sports Medicine, University
of Melbourne, Melbourne,
Victoria, Australia
3
Department of Life Sciences,
Roehampton University,
London, UK
4
INSIGHT Research Centre,
Dublin City University, Dublin,
Ireland
5
School of Health and Human
Performance, Dublin City
University, Dublin, Ireland
6
Department of Medicine,
University College Cork, Cork,
Ireland
Correspondence to
Dr A Franklyn-Miller;
[email protected]
Accepted 8 September 2016
Published Online First
6 October 2016
To cite: Franklyn-Miller A,
Richter C, King E, et al.
Br J Sports Med
2017;51:460–468.
1 of 10
ABSTRACT
Background Athletic groin pain (AGP) is prevalent in
sports involving repeated accelerations, decelerations,
kicking and change-of-direction movements. Clinical and
radiological examinations lack the ability to assess
pathomechanics of AGP, but three-dimensional
biomechanical movement analysis may be an important
innovation.
Aim The primary aim was to describe and analyse
movements used by patients with AGP during a
maximum effort change-of-direction task. The secondary
aim was to determine if specific anatomical diagnoses
were related to a distinct movement strategy.
Methods 322 athletes with a current symptom of
chronic AGP participated. Structured and standardised
clinical assessments and radiological examinations were
performed on all participants. Additionally, each
participant performed multiple repetitions of a planned
maximum effort change-of-direction task during which
whole body kinematics were recorded. Kinematic and
kinetic data were examined using continuous waveform
analysis techniques in combination with a subgroup
design that used gap statistic and hierarchical clustering.
Results Three subgroups (clusters) were identified.
Kinematic and kinetic measures of the clusters differed
strongly in patterns observed in thorax, pelvis, hip, knee
and ankle. Cluster 1 (40%) was characterised by
increased ankle eversion, external rotation and knee
internal rotation and greater knee work. Cluster 2 (15%)
was characterised by increased hip flexion, pelvis
contralateral drop, thorax tilt and increased hip work.
Cluster 3 (45%) was characterised by high ankle
dorsiflexion, thorax contralateral drop, ankle work and
prolonged ground contact time. No correlation was
observed between movement clusters and clinically
palpated location of the participant’s pain.
Conclusions We identified three distinct movement
strategies among athletes with long-standing groin pain
during a maximum effort change-of-direction task These
movement strategies were not related to clinical
assessment findings but highlighted targets for
rehabilitation in response to possible propagative
mechanisms.
Trial registration number NCT02437942,
pre results.
INTRODUCTION
Athletic groin pain (AGP) is a common chronic
presentation in professional and amateur sport.1 2
A recent systematic review in football2 reported an
incidence of AGP between 0.2 and 2.1/1000 hour
in men; hip/groin injuries were the third most
common injuries (14%). Similar incidences rates
are reported in field sports such as Rugby Union,3 4
Australian Rules Football5 and Gaelic football6 7
which share the common requirement of acceleration, deceleration, kicking and ‘cutting’ (movements combining deceleration and acceleration
with a change of direction).
Research into AGP to date has focused on trying
to localise the ‘injured’ or ‘painful’ structure clinically,8–10 radiologically11–13 and surgically.14–18
Existing clinical examination of patients with AGP
has been largely confined to palpation for pain,19–21
range of motion20 and dynamometer-based strength
testing in a single plane.22 23 Biomechanical segmental coordination (as described by the relationships
between segments articulating at the hip, knee and
ankle joints) during movements has not been
considered.
Segmental coordination is intrinsic to an athlete’s
ability to control change of direction and produce
power to execute movement.24 25 A loss of segmental coordination may lead to tissue injury, if the
magnitude, rate and direction of the loading of
muscles around a joint or joints exceeds that of
tissue tolerance.26 This coordination can be examined using a three-dimensional motion analysis
system. Three-dimensional motion analysis systems
have successfully identified pathomechanics of
anterior cruciate ligament injuries27 and might also
help explain causes of painful structures encountered in AGP. Therefore, studying the movement
strategies during a multidirectional movement task
might reveal potential injury mechanisms.
Our primary aim was to describe and analyse the
movement pattern used by patients with AGP during
a planned maximum effort change-of-direction task.
Our secondary aim was to determine if specific
anatomical diagnoses related to a distinct movement
pattern.
METHODS
Participants
In total, 382 consecutive male participants presenting
with chronic AGP were recruited. All players were
experienced multidirectional field athletes. The
Sports Surgery Clinic Hospital Ethics Committee
approved the study (Ref 25EF011) and all participants signed informed consent. The study was registered at Clinicaltrials.gov (NCT02437942).
Franklyn-Miller A, et al. Br J Sports Med 2017;51:460–468. doi:10.1136/bjsports-2016-096050
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Original article
Clinical examination
A detailed history, clinical examination,10 Hip and Groin
Outcome Score28 and magnetic resonance pelvis imaging was
carried out on each participant, the methodology and results of
which are reported previously.29 This combined assessment,
resulted in an anatomical clinical diagnosis for each participant.
Biomechanical examination
Participants undertook a standardised warm-up (five body
weight squats and five submaximal countermovement jumps).30
The multidirectional test protocol involved three maximal effort
trials (left and right limb turning) of a planned 110° cutting
manoeuvre (figure 1A).31 The order of which turn to use first
was randomised. Instructions given were as follows: run as fast
as possible towards the cone (red triangle; figure 1A), make a
single complete foot contact in front of the cone and run maximally to the finish line and plant with the outside foot (when
cutting left, plant with the right and vice versa). Each participant undertook two submaximal practice trials of the cut before
test trials were captured. A 1-min recovery was taken between
trials.
Data acquisition and analysis
An eight-camera motion analysis system (Bonita-B10, Vicon
Motion Systems, UK), synchronised with two force platforms
(BP400600, AMTI, USA), was used to record 24 reflective
markers (1.4 cm diameter) and ground reaction forces (GRFs).
Reflective markers were secured using tape, at bony landmarks
on the lower limbs, pelvis32 and trunk (modified Plug-in-Gait
marker set to include the trunk) (figure 1B). A software package
(Nexus 1.8.5, Vicon Motion Systems, UK) controlled simultaneous collection of motion and force data (200 Hz, 1000 Hz),
which were filtered using a fourth-order low-pass Butterworth
filter (cut-off frequency of 15 Hz33). Standard inverse dynamics
analysis was used to calculate tri-planar external joint
moments.34 The start and end of the test (cutting) movement
was defined by the GRF (>5N). Curves were normalised to 101
frames and landmark registered35 to the start of the acceleration
phase (last positive anterior GRF before negative peak; at 46%
of the movement cycle) (figure 2). This process aligned the start
of the acceleration phase (47%) for every participant, and it
accounts for differences in braking/deceleration and acceleration
phase across the participant during a continuous waveform
analysis.
Joint work was calculated as the sum of the moment (m)
multiplied with the change in angle (σ) of all three planes
( p; flexion, adduction, internal rotation) for every time point (t)
over the cycle for each joint ( j: ankle, knee and hip), generating
a single measure for joint work (equations (1) and (2)).
workj ¼ workj;flex þ workj;abd þ workj;rot
ð1Þ
Where
workj;p ¼
101 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2
X
(mj;p (t) (sj;p (t) sj;p (t 1)))
ð2Þ
t¼1
As calculated, joint work refers to the amount of work done by
all muscles acting across a joint.
Clustering
The data were examined for subgroups (clusters), using kinematic (ankle, knee, hip, pelvis and thorax angles) and kinetic
variables (ankle, knee and hip moments) using only the painful
side (by clinical palpation) for analysis. Where pain was found
on both sides, we randomly selected the left or right side.
Where no palpation pain was identified, the side of MRI confirmed pathology (eg, hip injury or aponeurosis injury) was
used. Again, where both sides were affected, we randomly
selected the left or right side.
For the clustering process, analysis of characterising phases
was used to calculate subject scores that describe the behaviour
of each participant within phases of variation of the selected
variables, which were combined into a matrix (number phases
of variation×number participant) and normalised for further
processing.36 The normalisation was performed by transforming
the combined similarity scores into their correlation matrix, to
quantify numerically the relationship between the behaviour of
participant, which cannot be described by distances of the generated similarity scores.37 Subsequently, gap statistic was used to
decide number of clusters (k) within the sample.38 Gap statistics
compare the within-cluster dispersion of a data set for a number
of requested cluster solution (eg, k=2 to 25) to the average
within-cluster dispersion. Following this differentiation of the
number of clusters, the correlation matrix was used as input for
a hierarchical clustering approach (figure 3).
Gap statistics suggested examining our data based on a 3
cluster solution, henceforth referred to as cluster 1, 2 and 3. A
full and detailed description of this methodology is available as
online supplementary appendix A.
Statistical analysis
To identify differences in kinematic and kinetic measures
between the clusters, statistical parametric mapping was
used.39 40 The Cohen’s D effect size was calculated in a
point-by-point manner to determine clinical relevance of a difference (d>0.5=medium; d>0.7=strong).41 Significant differences with a Cohen’s D <0.5 were discarded. For the discrete
work measures, an analysis of variance (ANOVA) was used to
identify differences between the clusters, and a post hoc test was
performed if significant differences were found. To compare the
interrelation between the identified clusters and the clinical
diagnoses, a contingency table (cross tabulation) was used. The
significance of this relationship was assessed using Pearson’s χ2
test. All data processing and statistical analyses were performed
using MATLAB (R2015a, MathWorks , USA).
RESULTS
Participants were aged 27.6 (±7.6) years old, 180 (±6.0) cm
tall and 81.9 (±9.4) kg with a median time of 36 (IQR 16–75)
weeks between onset of symptoms and presentation. From 382
original participants, 322 participants were used for the analysis.
A total of 31 participants had missing motion analysis data (they
did not attend the motion analysis appointment). While all
remaining participants were able to perform the cutting task, 29
had partial or double leg force plate contacts and had to be
excluded from further analysis.
Three distinct movement strategies (clusters) were identified
with differences in braking and acceleration phases by joint and
plane of movement kinematics (table 1 and figure 4A–E) and
kinetics by joint and plane (table 2 and figure 5A–E).
Each cluster is pictorially represented in sagittal (figure 6) and
frontal (figure 7) plane highlighting gross differences, and in
comparison of moment and total work done by the joints
(figure 8)—all descriptions of differences are in comparison to
the other clusters. A detailed description of findings can be
found in table 1 and 2.
Franklyn-Miller A, et al. Br J Sports Med 2017;51:460–468. doi:10.1136/bjsports-2016-096050
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Original article
Figure 1 A,B Illustration of the
multidirectional movement task and a
schematic illustration of the landmarks
of the marker setup.
Figure 2 Illustration of the mean and SD of the raw and landmarked
ground reaction force curve.
Figure 3 Representation of the splitting behaviour of the sample for
increasing number of clusters.
Cluster 1
Cluster 1 represents 40% (n=128) of the cohort, and the main
cluster features are increased ankle eversion, external rotation
and knee internal rotation (table 1) throughout the braking and
acceleration phase. In addition, cluster 1 demonstrated a high
degree of hip internal rotation (figure 4B). The thorax segment
demonstrates the smallest anterior tilt and medium ipsilateral
drop throughout the braking phase, while it features late
turning rotation towards the direction of travel in the acceleration phase (figure 4E) (table 1).
These kinematic features map with the kinetic differences
(table 2, figures 5 and 8) seen with cluster 1 demonstrating the
highest proportion of work done at the knee (effect size 0.72–0.92).
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Cluster 2
Cluster 2 represents 19% (n=62) of the cohort and is differentiated from cluster 1 and 3, predominantly by greater degrees of
hip flexion (effect size 0.86–1.41) throughout braking and acceleration (table 1). At the pelvis, we observed an increased anterior tilt
(figure 4E) as well as decreased contralateral drop and ipsilateral
rotation throughout the cycle. At the thorax, cluster 2 demonstrated an increased anterior tilt and ipsilateral drop (figure 4F).
Again this maps well with kinetics demonstrating the highest proportion of work done at the hip (table 2, figures 5 and 8).
Franklyn-Miller A, et al. Br J Sports Med 2017;51:460–468. doi:10.1136/bjsports-2016-096050
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Original article
Table 1 Description of kinematic differences between clusters 1, 2
and 3 with strong effect sizes
Joint
Findings Detailed Phase
Kinematics
Ankle
Dorsiflexion (+)
1,3>2
Eversion (+)
1>2,3
External rot. (−)
1>2,3
1,2>3
Valgus (−)
1,2>3
Internal rot. (+)
1>2,3
2>1>3
Abduction (−)
1,2>3
Internal rot. (+)
1,2>3
Pelvis
Anterior tilt (+)
2>1,3
Contralateral drop (+)
3>1>2
Ipsilateral rot. (−)
2>1>3
Thorax
Anterior tilt (+)
2>1,3
Ipsilateral drop (−)
2>1>3
Ipsilateral rot. (−)
1,2>3
2>1
Temporal
Time
Ground contact time
Effect
Clinical diagnoses
Knee
Flexion (+)
Hip
Flexion (+)
Mean
difference
towards the direction of travel (contralateral rotation) (table 1).
Cluster 3 featured the predominant work done at the ankle with
plantarflexor and evertor moments greater in the first part of
the cycle but smaller in the latter part 75–100% (table 2 and
figures 5 and 8).
1,2>3
1>2
3>2
1>2
1>3
1>2
1>3
24–63% 4.44
8–64% 6.77
1–100% 2.25
1–100% 1.87
1–100% 15.73
1–100% 13.66
0.56
0.76
1.13
0.86
1.27
1.06
1>3
2>3
1>3
2>3
1>2
1>3
12–88% 9.06
17–90% 8.36
7–12% 3.70
87–95% 2.95
1–100% 11.64
1–100% 13.64
0.90
0.81
0.55
0.51
1.11
1.14
2>1
2>3
1>3
1>3
2>3
1>2
2>3
1–100% 8.29
1–100% 17.67
1–100% 10.13
79–100% 4.03
76–100% 5.15
48–64% 6.64
32–75% 7.81
0.86
1.36
0.96
0.66
0.77
0.55
0.64
2>1
2>3
3>1
3>2
1>2
2>1
2>3
1>3
44–69% 3.72
29–100% 4.56
1–100% 4.95
1–100% 6.99
29–38% 2.99
21–89% 6.38
1–100% 13.31
1–100% 7.68
0.52
0.63
0.82
1.01
0.51
0.58
1.10
0.73
2>1
2>3
2>1
2>3
1>3
1>3
2>3
2>1
1–100% 8.92
48–100% 7.36
1–89% 6.73
1–100% 14.02
1–100% 7.57
1–73% 5.92
1–100% 9.28
95–100% 5.71
0.72
0.56
0.75
1.21
0.79
0.61
0.90
0.54
1>3
2>3
–
–
0.85
0.93
0.04
0.05
Mean values and effect sizes are calculated over the phase of significant difference.
Rot, rotation.
Cluster 3
Cluster 3 represents 41% (n=132) of the cohort and was characterised by reduced hip and knee flexion as well as increased
contralateral pelvis drop (table 1). In addition, cluster 3 demonstrated a decreased knee valgus, internal rotation of the hip, less
hip extension and greater hip adduction in the acceleration
phase than the other clusters. We further observed an increased
contralateral pelvis rotation. The thorax demonstrated increased
contralateral drop (during the acceleration phase) and rotation
Participants were classified into one or more of the five anatomical diagnoses, based on previous clinical and MRI examination
(table 3).29 A primary clinical diagnosis of pubic aponeurosis
injury was made in 194 (61%) cases (Dx aponeurosis); hip
injury was diagnosed in 63 (20%) cases (Dx hip)—these overlapped in 8 (2%) cases (Dx aponeurosis and Dx hip). Eight
patients (2%) were referred for arthroscopic hip evaluation (Dx
hipflex). Adductor injury was diagnosed in 45 (14%) cases (Dx
adductor), and inguinal injury in 2 (1%) cases (all related to the
ilioinguinal nerve; Dx inguinal). The diagnosis Dx aponeurosis
and Dx adductor and Dx aponeurosis and Dx hipflex was made
each in one case. These are explored in detail in a complementary paper.29 No significant relationship (p>0.644) exists
between movement cluster and the anatomical diagnoses. The
most common anatomical diagnosis of aponeurosis injury was
evenly distributed between movement clusters (table 3).
DISCUSSION
This is the first study to examine movement patterns in participants with AGP during a change-of-direction task. While performing a planned maximum effort change-of-direction task,
participants with a current symptom of AGP could be characterised as using one of three distinct movement strategies. These
strategies may represent different mechanisms of distributing the
resultant load, between segments that is, a thorax, hip, knee or
ankle dominant strategy that could lead to tissue overload and
propagation of AGP or may be compensatory due to the injured
structure being unable to tolerate that load or the pattern of
neuromuscular control altered.42
Underlying mechanism
Hip joint
Data are not available to relate kinematic measures to the resulting hip joint force for change-of-direction tasks; however, any
change in hip flexion angle will alter muscular action43 and
ultimately the resultant hip joint force.44 45 Hip flexion differed
strongly between clusters over the whole movement cycle
(cluster 2>1>3), with a mean difference of 17° between cluster
2 and 3. During hip flexion, the vector of the resultant joint
force fluctuates.46 This implies large differences in anterior hip
joint forces between the clusters. In particular, high shear forces
might present a risk in overloading stabilising muscle, as the cartilaginous surface of hip is designed to primarily tolerate compressive load.47 This was previously demonstrated during
walking where Lewis et al45 reported significant changes (24%)
in anterior hip joint force (forces through the acetabulum and
anterior pubic ramus) with only a 2° reduction in hip extension
angle. The different hip flexion angles found among clusters are
likely to alter the hip shear force and suggest differing sites of
load.
Proximal segments
The thorax segment demonstrated large differences in all three
planes between the clusters. The thorax accounts for upwards
of 35% of body mass,34 its position is influenced by pelvis
orientation and controlled in part by the abdominal muscles
(internal and external obliques, rectus abdominis and
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Figure 4 Graphical representations of joint kinematics in each plane. Black continuous line represents cluster 1, red dotted line cluster 2 and blue
broken line cluster 3. Below each graph, the shaded bars represent significant differences between clusters. Where (A) represents ankle, (B)
represents knee, (C) represents hip, (D) represents pelvis and (E) represents thorax.
transversus abdominis), which form the inguinal ligament superiorly and join the tendinous fascia of the common adductors
inferiorly to form an aponeurosis at the pubic symphysis.48 In
our cohort, we identified this as the primary site of pain in
>60% of cases.
Greater thorax anterior tilt (cluster 2) has been associated
with greater adductor longus activation49 and greater activation
of the hip extensors. To date, no relationship has been demonstrated between thorax angles and hip or groin injury; however,
studies have demonstrated an association between thorax angles
and knee injury.50 51 We observed significant differences in magnitude and orientation of thorax and pelvis angles in the sagittal
plane between the clusters, demonstrating an independent
behaviour between thorax and pelvis where pelvic sagittal tilt is
independent of torso movement. This differs from Houck
et al,52 who reported that thorax and pelvis could be considered
as one segment—our findings indicate the opposite.
The relationship between interdependent body segments,
with respect to joint position and segmental work, is known as
dynamic coupling53 or intersegmental linkage,54 and as such,
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the influence of the position of the thorax55–58 must be considered in analysis of pelvic and lower limb biomechanics. The
pubic symphysis can be thought of as the pivot or fulcrum
around which many of these forces are exerted59 and pain in
the pubic symphysis and rami is a common clinical sign in AGP.
Our cohort demonstrated different movement strategies
between thorax and pelvis (figure 4E). The range of variation in
these relationships could alter how the force is transmitted to
the pubic bone by the displacement of centre of mass from
centre of pressure, which alters the distribution of load by joint
to differ. Increases in shear and compressive loading at the
pubic symphysis, rectus abdominis and adductor attachments
could also change the relationship between risk of injury to
on-field training load. We propose that such a change in loading
leads directly to the pubic bone oedema often seen on MRI
imaging in this condition60 referred to incorrectly as osteitis
pubis.61 It follows that a degree of bone oedema occurs in
asymptomatic athletes by a similar mechanism of multidirectional loading13 62 and has been demonstrated not to resolve
post-surgery for AGP.63 64
Franklyn-Miller A, et al. Br J Sports Med 2017;51:460–468. doi:10.1136/bjsports-2016-096050
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Original article
Table 2 Descriptive of kinetic differences between the cluster 1, 2
and 3
Measure
Findings Detailed Phase
Kinetics
GRF
X
1,2>3
Y
3>2
3>1,2
Z
2>3
3>2
1,2>3
Ankle
Plantar moment (+)
1,3>2
Plantar moment (+)
1,2>3
Evertor moment (+)
1,3>2
Ex. rotator moment (−)
2>3
2>3
1,2>3
Knee
Extensor moment (+)
Extensor moment (+)
2>3
1,2>3
Valgus moment (+)
2,3>1
1>2,3
1,2>3
Int. rotator moment (+)
Hip
Extensor moment (+)
3>1,2
2>1>3
Adductor moment (−)
1,2>3
Int. rotator moment (+)
1>3
Mean
difference Effect
1>3
2>3
3>2
3>1
3>2
2>3
3>2
1>3
2>3
10–30%
12–22%
91–98%
43–56%
31–66%
84–92%
38–60%
76–93%
79–97%
0.73
0.82
0.25
1.29
1.87
0.99
1.98
1.66
1.56
0.56
0.52
0.53
0.54
0.77
0.53
0.68
0.56
0.64
1>2
3>2
1>3
2>3
1>2
3>2
2>3
2>3
1>3
2>3
2>3
1>3
2>3
2>1
3>1
1>2
1>3
1>3
1>3
2>3
3>1
3>2
10–75%
14–64%*
79–87%
86–100%
15–70%
19–68%
88–93%
10–18%
85–94%
94–98%
38–55%
74–88%
78–84%
1–6%
2–7%
19–71%
16–45%
70–80%
91–100%
93–100%
85–90%
92–97%
5.10
4.53
4.26
2.63
1.52
2.05
0.70
1.02
0.88
0.60
6.00
5.09
4.73
3.12
2.79
5.50
6.30
2.97
2.54
2.52
0.61
0.57
0.71
0.57
0.52
0.59
0.59
0.73
0.52
0.53
0.65
0.51
0.60
0.59
0.51
0.77
0.57
0.77
0.72
0.54
0.71
0.65
0.51
0.53
2<1
2<3
1<3
1>3
2>3
1>3
30–88%* 6.01
9–94%* 12.22
43–90% 8.01
90–98% 3.11
83–100% 3.21
69–75% 1.36
0.54
0.85
0.70
0.57
0.51
0.57
*Combines two phases of differences.
GRF, ground reaction forces; ex, external; int, internal
Distal joints
The ankle joint is often not considered when examining hip or
knee injuries. However, the ankle joint determines how GRFs
travel to proximal joints. The kinematics of the ankle joint differed strongly in all three planes in magnitude and shape.
Cluster 1 demonstrated much larger magnitudes in eversion and
external rotation and demonstrated medium ankle work in comparison with cluster 2 (least work done) and 3 (largest work
done), while all three clusters differ in their ankle flexion
angles. This suggests that each of the identified ankle joint strategies induce forces to proximal joints differently.
Relationship between the movement strategy and clinical
diagnosis
We expected to see a relationship between cluster type and the
specific anatomical diagnosis, which was not the case. If AGP is
the result of uncoupling of segmental control in repetitive multidirectional movements, a continuum of musculotendinous and
bony overload may be a more appropriate aetiological model.
Our belief is that a margin of tolerance exists anatomically and
biomechanically for the task execution of propagative movements for a given individual. This margin diminishes with
increased training load, genetic anatomical variation, deviation
of movement pattern or other extrinsic factors.
Exceeding this margin can lead to subcritical tissue overload
—which we propose as a biomechanical overload, the mechanism for AGP development. However, the current study does not
examine this assumption, and a prospective study is needed to
examine this hypothesis, especially given the finding that there
was no relationship between technique and diagnosis.
We contend that the condition AGP is the emergence of a site
of tissue failure secondary to biomechanical overload. Once
exceeded, either due to training load, technical deficiencies or
underlying anatomical abnormality/injury, this presents as a pain
at the site of tissue failure.65 Coordinated muscle function
(strength, power, timing of activation and endurance) is intrinsic
to an athlete’s ability to control change of direction and
produce coordinated movement execution.24 25 Changing
strength in isolation, in runners, is insufficient to alter movement patterns and coaching intervention using biofeedback, and
intrinsic and extrinsic cueing is required.66–68 Little work has
been done on the changing and coaching of change-of-direction
mechanics to date in a rehabilitation setting. The differing
movement strategies shown in our cohort strengthens the argument for an approach to rehabilitation, which addresses these
deficiencies. Further work is required to assess the features of
the rehabilitation before intervention strategies can be developed. However, our research suggests that a ‘one-size fits all’
approach, focussing on hip strength is unlikely to be effective.
Existing non-surgical rehabilitation programmes mainly target
the adductor muscle, due to perceived strength deficit23 69 70 or
the abdominal muscles. In our study, hip adductor moment that
is, the force calculated using inverse dynamics exerted to either
oppose abduction or produce adduction is predominately, but
not exclusively, produced by the adductor muscle complex and
was used as a proxy for adductor strength. We did not identify
consistent weakness in this measure in any of the clusters as a
target for rehabilitation. This was similar in analysis of thorax/
pelvis motion. Although the existence of three distinct clusters
suggests possible differentiation of targets for rehabilitation,
further work is underway to determine the validity of this
approach, rather than the traditional focus on the painful
structure.
Biomechanical studies commonly analyse a selected discrete
point in combination with a single group design which assumes
that (i) differences in overload pattern can be described in a few
selected measures (ie, peak moment at peak knee flexion) and (ii)
the injury mechanism is homogeneous, respectively. Applying
such design may discard important information, and the inherent
differences in movement strategies across group members could
also act to mask each other’s overload mechanism.36 37 A more
advanced way of examining data is the use of continuous and
subgroup analysis designs, which has been reported to be
superior over a discrete single group design.37 71–74 As such,
these techniques were applied in this study.
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Figure 5 Graphical representations of joint kinetics in each plane, and centre of mass and ground reaction force. Black continuous line represents
cluster 1, red dotted line cluster 2 and blue broken line cluster 3. Below each graph, the shaded bars represent significant differences between
clusters. Where (A) represents ground reaction force, (B) represents centre of mass, (C) represents ankle, (D) represents knee and (E) represents hip.
Figure 6 Sagittal view of clusters at impact, 1% (left); start of acceleration phase, 46% (middle); and toe-off, 100% (right). The pelvis of every
skeleton has been locked to an x=0, y=0 and z=0 coordinate, while every rotation angle has been set to 0. This approach results in the best
visualisation in the sagittal plane. The black skeleton represents cluster 1, the red cluster 2 and the blue cluster 3.
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Figure 7 Frontal view of clusters at impact, 1% (left); start of acceleration phase, 46% (middle); and toe-off, 100% (right). The pelvis of every
skeleton has been locked to an x=0, y=0 and z=0 coordinate, while every rotation angle has been set to 0. This approach results in the best
visualisation in the frontal plane. The black skeleton represents cluster 1, the red cluster 2 and the blue cluster 3.
Figure 8 Box plot highlighting differences in work done in cluster 1, 2, 3. D, effect size.
Our speculation—a possible future
Table 3
(%*)
Relationship between cluster and anatomical diagnosis
Clinical diagnosis
Cluster1
Cluster2
Cluster3
Total
Dx aponeurosis
n=76 (59)
n=38 (61)
n=80 (61)
Dx adductor
Dx aponeurosis and
Dx hip
Dx hip
Dx inguinal
Dx hipflex
Dx aponeurosis and
Dx adductor
Dx aponeurosis and
Dx hipflex
Total
n=19 (15)
n=5 (4)
n=10 (16)
n=1 (1)
n=16 (12)
n=2 (2)
n=194
(61%)
n=45 (14%)
n=8 (2%)
n=23 (18)
n=0 (0)
n=4 (3)
n=0 (0)
n=10 (16)
n=1 (1)
n=1 (1)
n=1 (1)
n=30 (23)
n=1 (1)
n=3 (2)
n=0 (0)
n=63 (20%)
n=2 (1%)
n=8 (2%)
n=1 (0%)
n=1 (1)
n=0 (0)
n=0 (0)
n=1 (0%)
n=128 (40%)
n=62 (19%)
n=132 (41%)
n=322
*Where % in parenthesis represents % of diagnosis within cluster or % of total
number diagnosis.
The measurement of movement strategies and quality have been
used before in areas such as back pain.75 It has also been noted
that the site of pain perception often does not correlate with the
site or extent of anatomical injury in many areas.76–78 Perhaps
in our focus on the individual injury sites, we have lost sight of
the larger view, and we suggest that, in the future, research into
AGP should spend less introspective focus on trying to subclassify into anatomically painful structures15 but on the resolution
of painful propagative movements in rehabilitation and injury
prevention.
Our study does not include a comparison of normative (uninjured) participants. We exclude a comparison with normative
data for the current analysis as we were specifically looking at
categories within this cohort. AGP could originate from either
the inability to execute different movement strategies (eg, continuous overload of a tissue rather than distribution of load over
multiple tissues) or the execution of a task using the extreme
characteristics of a movement pattern. The question of ‘what is
an optimal movement’ remains. The literature does not satisfy
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the requirements of such an ‘optimal’ movement in any
change-of-direction challenge, although we have demonstrated
the performance determinants of this cutting manoeuvre previously in normal participants.31 The question remains, however,
whether variability within or between clusters is injurious or
protective and remains yet to be determined and is the subject
of ongoing work.
We tested our participants in a planned maximal effort on
clockwise and counter clockwise 110° change of direction. We
acknowledge that strategies employed in an unplanned reactive
cut79 may be more stressful in neuromuscular load80 and may
reveal different strategies.52
Funding This study was funded by the SSC, and as an industrial partner of
INSIGHT, Dublin City University and Science Foundation Ireland (SFI) under grant
number SFI/12/RC/2289.
Competing interests None declared.
Ethics approval Sports Surgery Clinic Hospital Ethics Board.
Provenance and peer review Not commissioned; externally peer reviewed.
Open Access This is an Open Access article distributed in accordance with the
Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which
permits others to distribute, remix, adapt, build upon this work non-commercially,
and license their derivative works on different terms, provided the original work is
properly cited and the use is non-commercial. See: http://creativecommons.org/
licenses/by-nc/4.0/
REFERENCES
CONCLUSIONS
This is the first cohort of patients with AGP in which multiplanar multijoint segmental control was assessed in a
change-of-direction manoeuvre. The participants were distributed among three distinct ‘clusters’ during the biomechanical
examination. Anatomical diagnoses were distributed among the
three clusters.
1
2
3
4
What are the findings?
5
▸ Patients with long-standing AGP were grouped into three
distinct biomechanical clusters during the performance of a
planned 110° cutting manoeuvre.
▸ Movement strategies differed in torso, pelvis, hip, knee and
ankle joint angle in multiple planes in a cutting manoeuvre.
▸ Distribution of lower limb joint work was significantly
different between each cluster in a planned 110° cutting
manoeuvre.
▸ There was no correlation seen between anatomical
subgrouping/entity and the biomechanical diagnosis.
6
7
8
9
10
11
12
13
How might it impact on clinical practice in the future?
14
▸ AGP may be thought of as musculotendinous and bony
overload resulting in painful structures without a specific
critical injury.
▸ Considering the three dimensional analysis of movement
pattern during a change of direction can give a meaningful
addition to a clinical examination.
▸ The three observed clusters should be explored as targets for
rehabilitation to guide return to pain-free play.
▸ The existing approach, of trying to identify anatomical
classification of painful, as opposed to injured structures
may give way to whole body focus on movement.
15
16
17
18
19
20
21
Twitter Follow Enda King @enda_king, Andy Franklyn-Miller @afranklynmiller
Acknowledgements The authors would like to thank Eadaoin Holland, Steven
West, Ciara Black, Eamon O’Reilly and Jenny Ward who assisted in the collection of
data and the many Sports Surgery Clinic (SSC) staff who contributed to the data
processing and all of the participants who agreed to take part in this study.
Contributors AF-M wrote the original draft in conjunction with CR. SG provided
specific additional material and writing in the discussion. ECF, EK, SS and KM were
integral to study design data collection and interpretation, statistical analysis and
editing of the manuscript substantially.
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Athletic groin pain (part 2): a prospective
cohort study on the biomechanical evaluation
of change of direction identifies three
clusters of movement patterns
A Franklyn-Miller, C Richter, E King, S Gore, K Moran, S Strike and E C
Falvey
Br J Sports Med 2017 51: 460-468 originally published online October 6,
2016
doi: 10.1136/bjsports-2016-096050
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